Dimension of a Set of Transformations

In summary, we know that the set R of all linear transformations from a p-dimensional vector space Z to Z has a dimension of p-squared. This is because each matrix in the basis is p-dimensional, resulting in a p-squared dimensional space. Furthermore, every matrix in this space represents a linear transformation, making the set R a set of linear matrices. Additionally, there exists a linear transformation that maps every vector in a vector space to zero, which is the analog of the zero matrix. The dimension of this space is indeed p-squared.
  • #1
jsgoodfella
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0
If we consider the set R of all linear transformations from an p-dimensional vector space Z to Z (T:Z -> Z), what do we know about the dimension of the set R?

In other words, what do we know about any basis for R? What are its properties?
 
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  • #2
Hint: those linear mappings can be represented by matrices. What's the dimension of this space of matrices?
 
  • #3
micromass said:
Hint: those linear mappings can be represented by matrices. What's the dimension of this space of matrices?

Well, we know that each matrix is pxp, because Z is p-dimensional. The dimension of this space of matrices is p-squared (for instance there are 4 basis matrices for the 2x2 case).
But how can we know that each matrix in the p^2 dimensional basis is a linear matrix?
 
  • #4
jsgoodfella said:
But how can we know that each matrix in the p^2 dimensional basis is a linear matrix?

Huh, what do you mean?? Every matrix is linear.
 
  • #5
Thanks for staying with me!

micromass said:
Huh, what do you mean?? Every matrix is linear.


I'm thinking of the general basis for p^2 - If p is two, then [{0,1};{0,0}] could be a matrix in the basis which doesn't represent a linear transformation. Do we just assume they are all linear?

On another note, is there a linear transformation that maps every vector in a vector space to zero?
 
  • #6
jsgoodfella said:
I'm thinking of the general basis for p^2 - If p is two, then [{0,1};{0,0}] could be a matrix in the basis which doesn't represent a linear transformation. Do we just assume they are all linear?

It does represent a linear function, namely f(x,y)=(y,0). Every matrix represents a linear function, that's why matrices are so important.

On another note, is there a linear transformation that maps every vector in a vector space to zero?

Yep, that is a linear transformation. It's the analog of the zero matrix.
 
  • #7
Thanks for your help!

Just to make sure, the dimension is p-squared, right?
 
  • #8
Right!
 

FAQ: Dimension of a Set of Transformations

1. What is the dimension of a set of transformations?

The dimension of a set of transformations refers to the number of independent parameters required to describe all possible transformations within the set. It is closely related to the number of degrees of freedom of the system being transformed.

2. How is the dimension of a set of transformations determined?

The dimension of a set of transformations can be determined by analyzing the number and type of constraints on the transformations within the set. The more constraints, the lower the dimension of the set.

3. Does the dimension of a set of transformations affect its complexity?

Yes, the dimension of a set of transformations can affect its complexity. Higher dimensions typically require more parameters to describe the transformations, making it more complex to analyze and understand.

4. Can a set of transformations have a fractional or non-integer dimension?

Yes, a set of transformations can have a fractional or non-integer dimension. This can occur when the transformations within the set are not completely independent from one another, resulting in a dimension that is not a whole number.

5. How does the dimension of a set of transformations relate to its applications in science?

The dimension of a set of transformations can have important implications for its applications in science. For example, higher dimensional sets of transformations may be necessary to accurately model complex systems or phenomena, while lower dimensional sets may be more appropriate for simpler systems.

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